Matrices equality

Eortify each of these sub-samples with the analyte(s) of interest such that the concentration of the analyte(s) in the matrix equals the estimated limit of quantification (ELOQ). 

Since S is a symmetric matrix equal to Q(0), these equalities show that the off-diagonal blocks must vanish at x = 0, and hence that there is no instantaneous coupling between variables of opposite parity. The symmetry or asymmetry of the block matrices in the grouped representation is a convenient way of visualizing the parity results that follow. 

In contrast to correlation matrix the covariance matrix is scale-dependent. In case of autoscaled variables the covariance matrix equals the correlation matrix. 

In a previous chapter we noted that by augmenting the matrix of coefficients with unit matrix (i.e., one that has all the members equal to zero except on the main diagonal, where the members of the matrix equal unity), we could arrive at the solution to the simultaneous equations that were presented. Since simultaneous equations are, in one sense, a special case of regression (i.e., the case where there are no degrees of freedom for error), it is still appropriate to discuss a few odds and ends that were left dangling. 

This is where we see the convergence of Statistics and Chemometrics. The cross-product matrix, which appears so often in Chemometric calculations and is so casually used in Chemometrics, thus has a very close and fundamental connection to what is one of the most basic operations of Statistics, much though some Chemometricians try to deny any connection. That relationship is that the sums of squares and cross-products in the (as per the Chemometric development of equation 70-10) cross-product matrix equals the sum of squares of the original data (as per the Statistics of equation 70-20). These relationships are not approximations, and not within statistical variation , but, as we have shown, are mathematically (algebraically) exact quantities. 

We have considered in some detail in Section 4.2 the case where the random vector Y of n ancillary or dependent variables relates linearly to those of a vector X of n principal or independent variables (e.g., raw data) with covariance matrix L through the matrix equality... 

Grouping all similar equations in a matrix equality gives... 

In a casual way one could state that the rank of the matrix equals the number of different species that exist in the mixture. However, such a statement is not generally true and needs to be qualified in several ways ... 

Faced with the need of obtaining more transportation fuels from a barrel of crude, Ashland developed the Reduced Crude Conversion Process (RCC ). To support this development, a residuum or reduced crude cracking catalyst was developed and over 1,000 tons were produced and employed in commercial operation. The catalyst possessed a large pore volume, dual pore structure, an Ultrastable Y zeolite with an acidic matrix equal in acidity to the acidity of the zeolite, and was partially treated with rare earth to enhance cracking activity and to resist vanadium poisoning. 

Fibers extend the entire length of the composite, so that at any section the area fractions occupied by fibers and matrix equal their respective volume fractions, Vf and = 1 - V/. The total stress, cti, must then equal the weighted sum of stresses in fibers and matrix, ct/i, and ct i, respectively ... 

The short estimator, bi is biased E[bi] = Pi + Pi.2P2- It s variance is ct2(X1 X1)"1. It s easy to show that this latter variance is smaller. You can do that by comparing the inverses of the two matrices. The inverse of the first matrix equals the inverse of the second one minus a positive definite matrix, which makes the inverse smaller hence the original matrix is larger - Var[bi.2] > Var[bi]. But, since bi is biased, the variance is not its mean squared eiTor. The mean squared eiTor of bj is Var[bi] + biasxbias. The second term is Pi.2P2P2 Pi.2 - When this is added to the variance, the sum may be larger or smaller than Var[bi 2] it depends on the data and on the parameters, p2. The important point is that the mean squared error of the biased estimator may be smaller than that of the unbiased estimator. 

The determinant of a matrix equals the product of its characteristic roots, so the log determinant equals the sum of the logs of the roots. The characteristic roots of the matrix above remain to be detennined. As shown in the exercise, T-1 of the T roots equal 1. Therefore, the logs of these roots are zero, so the log-detenninant equals the log of the remaining root. It remains only to find the other characteristic root. Premultiply the result (o 2/a 2)ii c = (A-1 )c by i to obtain (a 2/a 2)i ii c = (A-l)i c. 

The determinant of a block-diagonal matrix equals the product of the block determinants (Section 1.2). Therefore (2.17) gives... 

Use the theorem of Problem 2.25 to show that the product of the eigenvalues of a matrix equals its determinant. 

For any equilibrium, either intra- or inter-molecular, the block of the superoperator X, which is concerned with the eigenvalue —1 of the superoperator F°, is nonpositive. One may rigorously prove this point by using Levy-Hadamard s theorem (e.g. reference 49). It is also necessary to consider that the sum of the moduli of the elements in each of the rows of the matrix X", from equation (118), is not larger than the modulus of the corresponding diagonal element of the K matrix [equation (119)]. The inequality results from the fact that in the basis set of product spin functions the sum of the moduli of the elements in each of the rows of the Y( matrix equals either 0 or 1. In addition, if any of the rows of the Y( matrix has non-zero elements, then the same row in the Y q matrix, where qj = 1 if j = 2 or else qj = 2 if j = 1, contains only zeros. 

Matrix and vector multiplication using the dot product is denoted by the symbol between matrices. It is only possible to multiply two matrices together if the number of columns of the first matrix equal the number of rows of the second matrix. The number of rows of the product will equal the number of rows of the first matrix, and the number of columns equal the... 

The concept of composition is an important one. There are many alternative ways of expressing the same idea, that of rank being popular also, which derives from matrices ideally the rank of a matrix equals the number of independent components or nonzero... 

Matrices A and B can be multiplied provided that they are conformable in the order written, that is, that the number of columns column order) of the first matrix equals the number of rows row order) of the second. The product C of a m x n matrix A by a n x p matrix B has the elements... 

Interpreting this matrix equality as described in Eq. (A. 1-6), we obtain four equations for the four unknown submatrices a, /3, 7, and 5 ... 

The correspondence between mathematical graph theory and classical chemical structure theory is manifested in Table I. A widely used mathematical representation for graphs is the adjacency matrix (A). The rank of this matrix equals the number of the vertices (atoms), and its entries a,y are equal to either 0 or 1 ... 

Derived from the -> molecular graph <5, the adjacency matrix A represents the whole set of connections between adjacent pairs of atoms [Trinajstic, 1992]. The entries Oy of the matrix equal one if vertices v, and Vy are adjacent (i.e. the atoms / and j are bonded) and zero otherwise. The adjacency matrix is symmetric with dimension A x A, where A is the number of atoms and it is usually derived from an -> H-depleted molecular graph. 

To the same order of approximation the diagonalisation matrix equals Dp = 1 + S f and the Hilbert space U 0) operator becomes... 

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